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    Support vector machine regression for project control forecasting

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    Publication type
    Journal article with impact factor
    Author
    Wauters, Mathieu
    Vanhoucke, Mario
    Publication Year
    2014
    Journal
    Automation in Construction
    Publication Volume
    47
    Publication Issue
    November
    Publication Begin page
    92
    Publication End page
    106
    
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    Abstract
    Support Vector Machines are methods that stem from Artificial Intelligence and attempt to learn the relation between data inputs and one or multiple output values. However, the application of these methods has barely been explored in a project control context. In this paper, a forecasting analysis is presented that compares the proposed Support Vector Regression model with the best performing Earned Value and Earned Schedule methods. The parameters of the SVM are tuned using a cross-validation and grid search procedure, after which a large computational experiment is conducted. The results show that the Support Vector Machine Regression outperforms the currently available forecasting methods. Additionally, a robustness experiment has been set up to investigate the performance of the proposed method when the discrepancy between training and test set becomes larger.
    Keyword
    Operations & Supply Chain Management, Earned Value Management (EVM), Support Vector Regression (SVR), Prediction
    Knowledge Domain/Industry
    Operations & Supply Chain Management
    DOI
    10.1016/j.autcon.2014.07.014
    URI
    http://hdl.handle.net/20.500.12127/5020
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.autcon.2014.07.014
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